Location-based conversational understanding may provide a mechanism for leveraging environmental contexts to improve query execution and results. Conventional speech recognition programs do not have techniques for leveraging information (e.g., speech utterances, geographical data, acoustic environments of certain locations, typical queries made from a particular location) from one user to another to improve the quality and accuracy of new queries from new and/or existing users. In some situations, speech-to-text conversions must be made without the benefit of using similar, potentially related queries to aid in understanding.
Speech-to-text conversion (i.e., speech recognition) may comprise converting a spoken phrase into a text phrase that may be processed by a computing system. Acoustic modeling and/or language modeling may be used in modern statistic-based speech recognition algorithms. Hidden Markov models (HMMs) are widely used in many conventional systems. HMMs may comprise statistical models that may output a sequence of symbols or quantities. HMMs may be used in speech recognition because a speech signal may be viewed as a piecewise stationary signal or a short-time stationary signal. In a short-time (e.g., 10 milliseconds), speech may be approximated as a stationary process. Speech may thus be thought of as a Markov model for many stochastic purposes.